MSc: Advanced Information Retrieval

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Advanced Information Retrieval

  • Course name: Advanced Information Retrieval
  • Code discipline: N/A
  • Subject area: Computer systems organization; Information systems; Real-time systems; Information retrieval; World Wide Web

Short Description

This course covers the following concepts: Data indexing; Recommendations; Relevance and ranking.

Prerequisites

Prerequisite subjects

  • CSE101 — Introduction to Programming I
  • CSE102 — Introduction to Programming II
  • CSE202 — Analytical Geometry and Linear Algebra I
  • CSE204 — Analytic Geometry And Linear Algebra II: matrix multiplication, matrix decomposition (SVD, ALS) and approximation (matrix norm), sparse matrix, stability of solution (decomposition), vector spaces, metric spaces, manifold, eigenvector and eigenvalue.
  • CSE113 — Philosophy I - (Discrete Math and Logic): graphs, trees, binary trees, balanced trees, metric (proximity) graphs, diameter, clique, path, shortest path.
  • CSE206 — Probability And Statistics: probability, likelihood, conditional probability, Bayesian rule, stochastic matrix and properties.
  • Analysis: DFT, [discrete] gradient.
  • 3blue1brown playlist on Linear Algebra can help to overview selected topics.
  • Actually, on their channel you can find almost any maths topic, e.g. Fourier Transform.
  • Gilbert Strang is one of the best human teachers of Algebra, if you prefer classic lectures to fancy videos.
  • This MIT course can help you with discrete objects.
  • For Russian readers there is a maths book from the course author with labs.
  • Also there is a very basic python-based course on maths with lots of relevant (and irrelevant) labs.
  • To have a better feeling of networking, please refer to this video lecture.
  • Kick start your numpy with the official quickstart guide.

Prerequisite topics

Course Topics

Course Sections and Topics
Section Topics within the section
Introduction. Crawling and quality basics
  1. Introduction to information retrieval
  2. Crawling
  3. Quality assessment
Text indexing and language processing
  1. Building inverted index for text documents. Boolean retrieval model.
  2. Language, tokenization, stemming, searching, scoring.
  3. Spellchecking.
  4. Language model. Topic model.
  5. Vector model for texts.
  6. ML for text embedding.
Advanced index data structures
  1. Vector-based tree data structures.
  2. Graph-based data structures. Inverted index and multi-index.
Advanced retrieval topics. Media retrieval
  1. Image and video processing
  2. Image understanding
  3. Video understanding
  4. Audio processing
  5. Speech-to-text
  6. Relevance feedback
  7. PageRank

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

The course is designed to prepare students to understand and learn contemporary tools of information retrieval systems. Students, who will later dedicate their engineering or scientific careers to implementation of search engines, social networks, recommender systems and other content services will obtain necessary knowledge and skills in designing and implementing essential parts of such systems.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

  • Terms and definitions used in area of information retrieval,
  • Search engine and recommender system essential parts,
  • Quality metrics of information retrieval systems,
  • Contemporary approaches to semantic data analysis,
  • Indexing strategies.

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

  • How to design a recommender system from scratch,
  • How to evaluate quality of a particular information retrieval system,
  • Core ideas and system implementation and maintenance,
  • How to identify and fix information retrieval system problems.

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

  • Build a recommender service from scratch,
  • Implement proper index for an unstructured dataset,
  • Plan quality measures for a new recommender service,
  • Run initial data analysis and problem evaluation for a business task, related to information retrieval.

Grading

Course grading range

Grade Range Description of performance
A. Excellent 84-100 -
B. Good 70-83 -
C. Satisfactory 60-69 -
D. Poor 0-59 -

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Labs/seminar classes 30
Interim performance assessment 0
Assessments (homework) 70
Exams 0

Recommendations for students on how to succeed in the course

Resources, literature and reference materials

Open access resources

  • "An Introduction to Information Retrieval" by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press (any edition)
  • “Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit,” Steven Bird, Ewan Klein, and Edward Loper. [link]

Closed access resources

Software and tools used within the course

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Activities within each section
Learning Activities Section 1 Section 2 Section 3 Section 4
Development of individual parts of software product code 1 1 1 1
Homework and group projects 1 1 1 1

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type Content Is Graded?
Question Enumerate limitations for web crawling. 1
Question Propose a strategy for A/B testing. 1
Question Propose recommender quality metric. 1
Question Implement DCG metric. 1
Question Discuss relevance metric. 1
Question Crawl website with respect to robots.txt. 1
Question Show how to parse a dynamic web page. 0
Question Provide a framework to accept/reject A/B testing results. 0
Question Compute DCG for an example query for random search engine. 0
Question Implement a metric for a recommender system. 0
Question Implement pFound. 0

Section 2

Activity Type Content Is Graded?
Question Build inverted index for a text. 1
Question Tokenize a text. 1
Question Implement simple spellchecker. 1
Question Embed the text with a model. 1
Question Build inverted index for a set of web pages. 0
Question build a distribution of stems/lexemes for a text. 0
Question Choose and implement persistent index for a given text collection. 0
Question Visualize a dataset for text classification. 0

Section 3

Activity Type Content Is Graded?
Question Build kd-tree index for a given dataset. 1
Question Why kd-trees work badly in 100-dimensional environment? 1
Question What is the difference between metric space and vector space? 1
Question Build (H)NSW index for a dataset. 0
Question Compare HNSW to Annoy index. 0
Question What are metric space index structures you know? 0

Section 4

Activity Type Content Is Graded?
Question Extract semantic information from images. 1
Question Build an image hash. 1
Question Build a spectral representation of a song. 1
Question Whats is relevance feedback? 1
Question Build a "search by color" feature. 0
Question Extract scenes from video. 0
Question Write a voice-controlled search. 0
Question Semantic search within unlabelled image dataset. 0

Final assessment

Section 1

  1. Implement text crawler for a news site.
  2. What is SBS (side-by-side) and how is it used in search engines?
  3. Compare pFound with CTR and with DCG.
  4. Explain how A/B testing works.

Section 2

  1. Explain how (and why) KD-trees work.
  2. What are weak places of inverted index?
  3. Compare different text vectorization approaches.
  4. Compare tolerant retrieval to spellchecking.

Section 3

  1. Compare inverted index to HNSW in terms of speed, memory consumption?
  2. Choose the best index for a given dataset.
  3. Implement range search in KD-tree.

Section 4

  1. What are the approaches to image understanding?
  2. How to cluster a video into scenes and shots?
  3. How speech-to-text technology works?
  4. How to build audio fingerprints?

The retake exam

Section 1

Section 2

Section 3

Section 4